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Data Informed Decision-Making

Explore data-driven decision-making processes, available tools, and strategies for using data to inform school improvement planning. Evaluate Hanover schools’ data practices and address challenges. Gain insights from principals on strengths and weaknesses of data-driven decision-making.

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Data Informed Decision-Making

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  1. Data Informed Decision-Making Leadership Retreat August, 2009 Presented by: Heather Causey, Rebecca Evan, Cheri Beth Fisher, and Sheree Shaw

  2. Questions we hope to address: • What is data driven decision-making? • What data are available to us? • What are Hanover schools doing with data? • What tools are available to assist me with data analysis? • How do we use these tools? • How can we promote discussions about data? • What can I take back to use in my school?

  3. “Without data, all anyone has are opinions. Data elevates the probability that you’ll make the right decision.” -W. Edwards Deming

  4. Data driven decision-making is a process that involves:

  5. 1. Mining (collecting and managing) the data

  6. 2.Analyzing data to create knowledge

  7. 3. Communicating data to support organizational learning

  8. 4. Using the data to inform school improvement planning

  9. In which area could your school use strengthening? Analyzing Data Mining Data Using data for school improvement Communicating Data

  10. Survey: • My staff is comfortable collecting data. • My staff is comfortable talking about data with their teams. • My staff members can manipulate their own students’ data. • My staff uses data to make daily instructional decisions. • I am satisfied with data-driven decision making at my school.

  11. What data are available to us in Hanover?

  12. What Principals Are Saying: Strengths in Data Driven Decision-Making • Gathering data • Analyzing benchmark data using ROS Works • Having conversations about data with teams or faculties • Grouping based on reading assessment data • Sharing data with all teachers who instruct that student • Setting goals/targets based on SOL test data

  13. What Principals Are Saying: Weaknesses/Challengesin Data Driven Decision-Making • Finding time to review data and reflect on what it means • Understanding the significance of data analysis as a diagnostic tool to assist students rather than a personal reflection on teachers • Having teachers see the big picture rather than simply focus on “their” students

  14. What Principals Are Saying: Weaknesses/Challengesin Data Driven Decision-Making • Knowing what to do after the analysis--determining what intervention to use/how to remediate when weaknesses are apparent in the data • Knowing what data to collect and analyze for grade levels without benchmark assessments • Determining if benchmark assessment data is a reliable indicator of SOL test performance

  15. Partner Talk How does your school currently collect, analyze, and use data to make instructional decisions?

  16. What’s HOT in Hanover?

  17. Student Response Systems

  18. Math Pre- and Post- Tests TfHS

  19. IEP Goal Data Behavioral: Academic:

  20. Data Boards Electronic Data Board example

  21. Involving Resource Teachers Grade levels can put strands of weaknesses on a Blackboard Discussion Board and anyone can add integration ideas to it.

  22. Student Data Folders - ElementaryStudent Led Conferences - Secondary

  23. Teacher Data Binders

  24. PALS Quick Checks

  25. Curriculum Based Measures (CBMs)

  26. CBMs with handheld devices

  27. Break!!

  28. ROS Data

  29. How can YOU use ROS? • Item analysis by student, class, or grade level • Grade level classroom comparisons by standard/strand • Student remediation grouping by weak strand performance • Subgroup reports (by counts or percentages) • Classroom assessments and keys added online

  30. Business Objects Core Reports

  31. Elementary Reading Secondary Math Case Studies

  32. Guiding Questions for Case Study Analysis • Which strands have students mastered with at least 80% proficiency? • Which strands require continued remediation? • How does individual class performance compare? • What next steps would you take as the school leader? teacher?

  33. What do you see?

  34. Ideas for Engaging Staff in Data Driven Decision-Making • Define top 10 common data analysis terms individually, then as a group to reach consensus • Give teachers access to ROS Works • Offer professional development on data analysis tools • Build common planning and remediation blocks into the master schedule • Structure conversations--develop guiding questions for data discussions to be used by teams • Develop common teacher data binders

  35. Mini Break Out Sessions • Practice with ROS Works • Practice with electronic data boards • Practice with student response systems

  36. What’s next? • What AHA moments did you have in this session? • What new ideas would like to take back and implement with your staff? • What training is needed to help with data-driven decision making for your staff?

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